Attrition in Randomized Control Trials: Using Tracking Information to Correct Bias

85 Pages Posted: 1 May 2017

See all articles by Teresa Molina Millán

Teresa Molina Millán

New University of Lisbon - Nova School of Business and Economics

Karen Macours

Paris School of Economics (PSE)

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Abstract

This paper starts from a review of RCT studies in development economics, and documents many studies largely ignore attrition once attrition rates are found balanced between treatment arms. The paper analyzes the implications of attrition for the internal and external validity of the results of a randomized experiment with balanced attrition rates, and proposes a new method to correct for attrition bias. We rely on a 10-years longitudinal data set with a final attrition rate of 10 percent, obtained after intensive tracking of migrants, and document the sensitivity of ITT estimates for schooling gains and labour market outcomes for a social program in Nicaragua. We find that not including those found during the intensive tracking leads to an overestimate of the ITT effects for the target population by more than 35 percent, and that selection into attrition is driven by observable baseline characteristics. We propose to correct for attrition using inverse probability weighting with estimates of weights that exploit the similarities between missing individuals and those found during an intensive tracking phase. We compare these estimates with alternative strategies using regression adjustment, standard weights, bounds or proxy information.

Keywords: survey non response, sample selectivity, randomized controlled trial, inverse probability weights

JEL Classification: O1, C93, C52

Suggested Citation

Molina Millán, Teresa and Macours, Karen, Attrition in Randomized Control Trials: Using Tracking Information to Correct Bias. IZA Discussion Paper No. 10711. Available at SSRN: https://ssrn.com/abstract=2960520

Teresa Molina Millán (Contact Author)

New University of Lisbon - Nova School of Business and Economics ( email )

Campus de Campolide
Lisbon, 1099-032
Portugal

Karen Macours

Paris School of Economics (PSE) ( email )

48 Boulevard Jourdan
Paris, 75014 75014
France

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